The climate is changing yet many rely on long term average temperature and precipitation data to get an idea of what is “normal” for a field. However, weather is complex and relying on station data for long term averages isn’t the best method. First, station data is valid for that point and that point only. Single-site station data does not represent the spatial coverage needed to understand historic yields in the context of weather. In addition, the maintenance of that station and its reliability is suspect at best. This means you could be dealing with a dataset that is flawed and may be missing data due to poor data quality.
But there is another way! Weather Decision Technologies has created the ability to interrogate weather data in a unique way that combines the very best of observation platforms from station data, to satellite, to radar for the best possible result. By applying advanced data and meteorological techniques, WDT has generated a massive historical database of daily weather variables around the globe at extremely high resolution. Utilizing past weather information helps seed companies and agriculture retailers understand weather’s role in yield and ultimately return on investment over an entire field vs over a point location or coarse grid.
Use cases for WDT's high resolution gridded dataset includes feed info into disease models, whereas retailers have the opportunity to look back and understand the weather conditions that led to a specific pest or disease outbreak. Also, feeding this weather information allows for seed companies to improve yield by more precisely placing seed in favorable fields with suggested planting dates.
Looking into the past is only a small part of the equation when it comes to understanding how to optimize weather data; whether it’s a seed, chemical, ag retailer, precision ag platform, OEM, agronomist, or grower. The same high resolution datasets that generate the past weather information feed all of WDT’s numerical weather models for forecast information which in turn can feed predictive analytics in agriculture.
